J. Eliashberg et al., MOVIEMOD: An implementable decision-support system for prerelease market evaluation of motion pictures, MARKET SCI, 19(3), 2000, pp. 226-243
In spite of the high financial stakes involved in marketing new motion pict
ures, marketing science models have not been applied to the prerelease mark
et evaluation of motion pictures. The motion picture industry poses some un
ique challenges. For example, the consumer adoption process for movies is v
ery sensitive to word-of-mouth interactions, which are difficult to measure
and predict before the movie has been released. In this article, we undert
ake the challenge to develop and implement MOVIEMOD-a prerelease market eva
luation model for the motion picture industry. MOVIEMOD is designed to gene
rate box-office forecasts and to support marketing decisions for a new movi
e after the movie has been produced (or when it is available in a rough cut
) but before it has been released. Unlike other forecasting models for moti
on pictures, the calibration of MOVIEMOD does not require any actual sales
data. Also, the data collection time for a product with a limited lifetime
such as a movie should not take too long. For MOVIEMOD it takes only three
hours in a "consumer clinic" to collect the data needed for the prediction
of box-office sales and the evaluation of alternative marketing plans.
The model is based on a behavioral representation of the consumer adoption
process for movies as a macroflow process. The heart of MOVIEMOD is an inte
ractive Markov chain model describing the macro-flow process. According to
this model, at any point in time with respect to the movie under study, a c
onsumer can be found in one of the following behavioral states: undecided,
considerer, rejecter, positive spreader, negative spreader, and inactive. T
he progression of consumers through the behavioral states depends on a set
of movie-specific factors that are related to the marketing mix, as well as
on a set of more general behavioral factors that characterize the movie-go
ing behavior in the population of interest. This interactive Markov chain m
odel allows us to account for word-of-mouth interactions among potential ad
opters and several types of word-of-mouth spreaders in the population. Mark
eting variables that influence the transitions among the states are movie t
heme acceptability, promotion strategy, distribution strategy, and the movi
e experience. The model is calibrated in a consumer clinic experiment. Resp
ondents fill out a questionnaire with general items related to their movie-
going and movie communication behavior, they are exposed to different sets
of information stimuli, they are actually shown the movie, and finally, the
y fill out postmovie evaluations, including word-of-mouth intentions. These
measures are used to estimate the word-of-mouth parameters and other behav
ioral factors, as well as the movie-specific parameters of the model.
MOVIEMOD produces forecasts of the awareness, adoption intention, and cumul
ative penetration for a new movie within the population of interest for a g
iven base marketing plan. It also provides diagnostic information on the li
kely impact of alternative marketing plans on the commercial performance of
a new movie. We describe two applications of MOVIEMOD: One is a pilot stud
y conducted without studio cooperation in the United States, and the other
is a full-fledged implementation conducted with cooperation of the movie's
distributor and exhibitor in the Netherlands. The implementations suggest t
hat MOVIEMOD produces reasonably accurate forecasts of box-office performan
ce. More importantly, the model offers the opportunity to simulate the effe
cts of alternative marketing plans. In the Dutch application, the effects o
f extra advertising, extra magazine articles, extra TV commercials, and hig
her trailer intensity (compared to the base marketing plan of the distribut
or) were analyzed. We demonstrate the value of these decision-support capab
ilities of MOVIEMOD in assisting managers to identify a final plan that res
ulted in an almost 50% increase in the Lest movie's revenue performance, co
mpared to the marketing plan initially contemplated. Management implemented
this recommended plan, which resulted in bur-office sales that were within
5% of the MOVIEMOD prediction. MOVIEMOD was also tested against several be
nchmark models, and its prediction was better in all cases.
An evaluation of MOVIEMOD jointly by the Dutch exhibitor and the distributo
r showed that both parties were positive about and appreciated its performa
nce as a decision-support tool. In particular, the distributor, who has mor
e stakes in the domestic performance of its movies, showed a great interest
in using MOYIEMOD for subsequent evaluations of new movies prior to their
release. Based on such evaluations and thr initial validation results, MOVI
EMOD can fruitfully (and inexpensively) be used to provide researchers and
managers with a deeper understanding of the factors that drive audience res
ponse to new motion pictures, and it can be instrumental in developing othe
r decision-support systems that can improve the odds of commercial success
of new experiential products.